This article will take you into the world of predictive analysis. We will learn why is important and what are its benefits. We will take a look at a few examples of businesses that use it, and most importantly we will explore the three common types of predictive analytical models used in predictive analytics – decision trees, regression, and neural networks. In addition to that, we will take a look at predictive analytics tools that are powered by even more models, such as classification models, clustering, forecast, outliers, and time-series models among many, as well as and 5 common predictive analytics algorithms that can be applied to a wide range of use cases.
What is predictive analysis?
Predictive analysis has been around for decades, especially in industries such as insurance and banking in insurance and credit scoring, and of course, let’s not forget the predictive models that help make weather forecasts. Nevertheless, more and more industries are jumping on a bandwagon – from video games development to decisions in customer service and diagnosing patients in health care, just to name a few.
When we work with predictive analytics we set the basis on historical data and we use statistical algorithms, modeling techniques, and machine learning techniques so we could predict the probability of a future outcome. The goal of prediction analysis is to provide the best evaluation of what will happen, for example, to improve efficiency or to reduce risks. All this is based on data patterns from current and previous events.
When working with the predictive analysis we have to combine a few techniques, which can include statistics, data mining, machine learning, and predictive modeling. For example, data mining could involve the analysis of large sets of data to detect specific patterns from it, and machine learning is often used to create predictive models by extracting patterns from large datasets. These predictive models can be used in data analytics applications, such as predicting prices and customer behavior, or for example assessing risk, or as “simple” as weather prediction, translating voice to text for mobile phone messaging, or text suggesting. Predictive models also help businesses manage inventory, develop marketing strategies, and even forecast sales, they can optimize processes and relationships – in short, predictive models can help businesses survive.
How different industries can use predictive analysis
We’ve already mentioned a few examples of how predictive analysis modeling can help businesses thrive, but let’s take a look at the role of predictive analytics as a decision-making tool in several different industries.
Predictive analysis in banking – credit scoring
Credit scoring is a form of artificial intelligence. It is based on predictive modeling that determines the likelihood of a customer defaulting on a credit obligation, becoming either delinquent or insolvent. The higher the customer’s credit score, the more certain the bank is of the customer’s creditworthiness and more likely is to give a loan.
Predictive analysis in supply chain
Predictive analysis is essential in manufacturing because it ensures the optimal use of resources in a supply chain – it determines optimal inventory levels so the business can satisfy demand while also minimizing the stock. Predictive analytics can determine detailed supply chain inventory requirements by region, location, and usage.
Predictive analysis in marketing
Predictive analysis helps marketing professionals to analyze how consumers have reacted to the overall economy when planning on a new campaign. With predictive analysis, they can better understand consumer interests based on past interactions and they can even segment audiences based on known interests and demographic information. This knowledge better equips marketers to serve targeted messaging at the right time and even on the right device.
Predictive analysis in healthcare
Even in healthcare, predictive analytics plays a vital role – it helps healthcare providers in decision-making, it improves patient outcomes with more personalized patient care and eventual earlier intervention, and also ensures reduced hospital costs.
Predictive analysis in video gaming
In video gaming, predictive analysis helps identify meaningful relationships, patterns, trends, and user behavior models from complex data sets. This helps creators guide service roadmaps and also creates automated anomaly detection systems. Therefore, predictive analysis in video gaming helps boost user engagement, which is one of the top priorities in the industry.
The three common techniques used in predictive analytics
There are three common types of predictive analytical models used in predictive analytics:
- decision trees,
- and neural networks.
Decision trees in predictive analysis
We’re sure you’ve already seen decision trees in a form of flow charts where you start at the top, and as you answer questions they lead you to subsequent questions, and at the end, you arrive at your answer. A decision tree, as the name signals, looks just like a tree – it has individual branches that indicate choices that you have when answering questions and leaves that represent a particular answer. Decision trees are one of the most popular and widespread methods of creating and visualizing predictive models and algorithms, and they are often used for handling non-linear data sets effectively.
Although decision trees can be complex, they are some of the simplest predictive analytical models used in predictive analytics because they’re relatively easy to understand and organize. In addition to their simplicity, one of their usefulness is using them when you need to make a decision in a short period of time. Decision trees are used in real life in many areas, such as engineering, civil planning, law, and business.
When you create a decision tree, you’re splitting a data population into smaller segments through two stages. In the first stage, where you create a training model of a decision tree, you build, test, and optimize your decision tree by using an existing collection of data. In the second stage, you actually start applying the decision tree to predict an outcome.
At this point, it is also worth mentioning we recognize different types of decision trees and they differentiate on what we’re trying to predict. One of them is the regression tree, which we use to predict continuous quantitative data. One example of a regression tree could be predicting a person’s income based on available information such as his occupation, age, and other continuous variables since the data we’re predicting falls into a category of continuous quantitative data. However, do not get allured into thinking that regression trees are reserved for numbers only – it is important that the data is categorized and you can use a regression tree. However, when you’re working with qualitative data, we use a classification tree or categorical variable decision tree. For instance, the categories can be yes or no. The categories mean that every stage of the decision process falls into one category, and there are no in-betweens. One example of a classification tree would be when trying to predict a medical diagnosis based on different symptoms or using demographic data to find prospective clients.
Regression in predictive analysis
Regression is one of the primary tools that are used the most in predictive analysis and statistics in general. Usually, we use it when there’s a linear relationship between the inputs and when we want to determine patterns in large sets of data. Regression works by figuring out a formula that represents the relationship between all the inputs found in the dataset. We observe the dependent variables and independent variables (variables that influence the dependent variable) and we evaluate if there is an association between them and what is the strength of the relationship. And these are actually also the main benefits of using regression analysis:
- it indicates important relationships between a dependent variable and independent variable,
- indicates the strength of the impact of independent variable on a dependent variable.
There are various kinds of regression techniques available to make predictions, such as:
- linear regression, which is one of the most widely known modeling techniques
- logistic regression, which is a type of regression that is used when the dependent variable is binary (true/false)
- polynomial regression, where the best fit line is not a straight line but is rather a curve that fits into the data points
- stepwise regression, which is used when we deal with multiple independent variables
- ridge regression, which is a technique used when we are dealing with highly correlated independent variables
- lasso regression, which is similar to the ridge regression, but it also penalizes the absolute size of the regression coefficients and is capable of reducing the variability and improving the accuracy of linear regression models
- elasticNet regression which is a hybrid of lasso and ridge regression techniques.
A form of predictive analytics – neural networks work by imitating the way the human brain works. Compared to linear regression, one of the most simplified and most often used predictive models, which uses only input and output nodes to make predictions, neural networks work with complex data relationships. In this manner, neural networks use artificial intelligence and pattern recognition and they work with hidden layers to make predictions more accurate.
Neural networks are recommended when you work with a large amount of data or when you need to make predictions rather than come up with explanations.
So, why don’t we always use neural networks as a form of predictive analytics, if they are so super-accurate? Well, for one thing, neural networks require huge amounts of computing power, which has cost requirements and not everyone has that at hand. Another important aspect, which can be an advantage as well as a disadvantage – neural networks require large data sets and your business might not have them.
So, how do neural networks work? First of all, there are three layers to the structure of a neural-network algorithm:
- The input layer that enters history data values into the next layer.
- The hidden layer is a key component of a neural network because of its complex functions that create predictors. A hidden layer consists of a set of nodes that are called neurons and they actually represent math functions that modify the input data. In general, more nodes and more layers allow the neural network to make much more complex calculations.
- The output layer: The predictions that are made in the hidden layer are collected to produce the final layer which is the model’s prediction.
So, in the hidden layer, each neuron takes into consideration a set of input values, which in translation means it gets linked to a “weight” (some sort of numerical value), which is acquired by either supervised or unsupervised training, and a value called “bias”. The network then chooses from an answer which is put together on the basis of that “weight” and “bias”. Thus, a neural network is nothing more than a network of equations, where each node ultimately determines which node in the following layer gets activated, until it reaches an output. Conceptually, that is the essence of a neural network.
Neural networks have advanced so much that there are now several types of neural networks, but here are three main types: Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN). They have led to revolutionary applications such as recommendation systems, image, video and audio recognition, and even autonomous driving.
What else should a data scientist know about predictive analysis?
In addition to all that had already been said, predictive analytics tools are powered by even more models and algorithms that can be applied to a wide range of use cases. A data scientist should empower the management so the best decision about the appropriate predictive modeling techniques for the company is made. The key is to get the most out of a predictive analytics solution and leverage data to make insightful decisions.
The classification model puts data in categories based on what it learns from historical data, and in a way, it is one of the simplest predictive analytics models. They are best to answer with yes or no questions, which provides a broad analysis that can be helpful for guiding decisive action and can be applied to a wide range of different industries.
Similar to the classification model, the clustering model sorts data into separate, nested smart groups based on similar characteristics, which optimizes strategic approaches for each group. This way we can identify customers with the same attributes and choose for example choose the best marketing approach for them, or we can cluster load applicants or identify areas in the city with a lower income or higher crime volume.
The forecast model is one of the most widely used predictive analytics models. The forecast model deals in metric value prediction and estimates a numeric value for new data based on learnings from historical data.
Compared to other models, the outliers model is different because its focus is on the anomalies in a dataset, such as for instance a spike in support calls that can signal a problem with a service or a product.
Time series model
With the time series model, as the name suggests, time is the parameter that is used as an input. We can, for example, use the last twelve months of data to develop a numerical metric and predict the next six weeks of data using that metric. For example, we can predict how many patients will come to the hospital in a given period, if we evaluate historic numbers, seasons of the year, and events that could impact the metric, such as holidays, weather etc.
As you can imagine, there are more models and techniques that can be used and we can even combine the ones we’ve mentioned to get a more precise prediction:
Data mining is a technique that combines statistics and machine learning to discover anomalies, patterns, and correlations in massive datasets. Data mining is very often integrated into other models and techniques because it enables noisy and unstructured data to be read as a pattern that can surface relevant insights. Exploratory data analysis (EDA) is one type of data mining technique that involves analyzing datasets to summarize their main characteristics, often with visual methods.
Data warehousing is the basis of most large-scale data mining efforts. Data warehouse presents a type of data management system designed to enable and support business intelligence efforts by centralizing and consolidating multiple data sources.
5 common predictive analytics algorithms
Since we’ve dived into predictive analytics, let’s just quickly glance through 5 common predictive analytics algorithms. In general, predictive analytics algorithms can be separated into machine learning and deep learning:
- Random forest is one of the most popular classification algorithms and is capable of both classification and regression and can therefore classify large amounts of data.
- Generalized linear model (GLM) and Generalized linear model for two values are advanced statistical modeling techniques formulated way back in 1972. A generalized linear model is an umbrella term that encompasses many other models, which allows the response variable y to have an error distribution other than a normal distribution.
- Gradient Boosted Model (GBM) is a machine learning technique used in regression and classification tasks, among others. It gives a prediction model in the form of an ensemble of weak prediction models, which are typically decision trees.
- K-Means is one of the popular, high-speed algorithms that is used for clustering model and involves placing unlabeled data points in separate groups based on similarities. This algorithm is used for the clustering model.
- The Prophet algorithm is an opensource and was developed by Facebook and is used in the time series and forecast models.
You have so many choices, right? So, how do you determine which predictive analytics technique or model is the best for your needs? You will answer this question when you identify what predictive questions you are looking to answer, and even more importantly, what you are looking to do with that information. You will have to consider the strengths and weaknesses of each technique and each model and you will be able to decide which one is best to use for your needs.